Biology subjects

George, D.

Publications and source records attributed to George, D..

12 recordsLinked to original sources

bioRxiv2025

Computational modeling of necrosis in neural organoids

Neural organoids (NOs), also known as brain organoids, are derived from human-induced pluripotent stem cells and are Microphysiological Systems (MPS) of the brain that can recapitulate key aspects of neurodevelopment. They enable in vitro studies of brain development and disease mechanisms, providing disease models for various neurodegenerative or neurodevelopmental/degenerative disorders like Alzheimers disease, microcephaly, and autism. There are many protocols to generate NOs with different complexities and sizes, varying from 400 m to several mm in diameter, with a starvation-induced necrotic core eventually forming depending on the diameter and culture conditions. Thus, they can benefit from vascularization and more optimal culture conditions. There have been several attempts to decrease necrosis while growing larger NOs, such as by using orbital shaking or 2D/3D microfluidic chips, but only with limited success. In this study, we describe a 3D finite element model to simulate O2 starvation-induced necrosis in NOs using the Damkohler Number (Da) and the Michaelis-Menten kinetics. We measured the necrotic areas in NOs using fluorescent imaging and used them to calibrate the model with a specific Da. Using these calibrated values, we systematically compared simulations of different NO culture methods--static, orbital shaking, and microfluidic flow around organoids--highlighting their relative impacts on nutrient diffusion and necrosis. We observed that these culture strategies cannot prevent necrosis beyond a diameter of [~]800 m. Based on these findings, we propose that 3D spatial perfusion, achieved through uniformly distributed fluidic capillaries within the NO, could significantly reduce necrosis. We conducted parametric studies on capillary spacing, density, and layout. Our calibrated model offers insights for designing next-generation microfabricated bioreactors and culture devices, not just for NOs but also for all 3D tissue engineering and organoid research.

bioengineering↗

bioRxiv2025

Neuromodulation in neural organoids with shell MEAs

Neural organoids (NOs) have emerged as important tissue engineering models for brain sciences and biocomputing. Establishing reliable relationships between stimulation and recording traces of electrical activity is essential to monitor the functionality of NOs, especially as it relates to realizing biocomputing paradigms such as reinforcement learning or stimulus discrimination. While researchers have demonstrated neuromodulation in NOs, they have primarily used 2D microelectrode arrays (MEAs) with limited access to the entire 3D contour of the NOs. Here, we report neuromodulation using tiny mimics of macroscale EEG caps or shell MEAs. Specifically, we observe that stimulating current within a specific range (20 to 30 {micro}A) induced a statistically significant increase in neuron firing rate when comparing the activity five seconds before and after stimulation. We observed neuromodulatory behavior using both three- and 16-electrode shells and could generate 3D spatiotemporal maps of neuromodulatory activity around the surface of the NO. Our studies demonstrate a methodology for investigating 3D spatiotemporal neuromodulation in organoids of broad relevance to biomedical engineering and biocomputing. One-Sentence SummaryNeuromodulation, an essential intelligence feature, was observed using 3D stimulation and recording from neural organoids.

bioengineering↗

bioRxiv2025

Epidermal Langerhans Cells Drive Painful Diabetic Neuropathy in a Sex-dependent Manner

The interaction between non-neuronal cells and nerve endings in the epidermis significantly influences the development of various diseases, including Painful Diabetic Neuropathy (PDN). PDN is a common and challenging complication of diabetes, characterized by changes in skin innervation accompanied by neuropathic pain. While there is growing evidence that epidermal non-neuronal cells, such as resident immune cells, play a crucial role in the progression of PDN, the underlying mechanisms of this neuropathy remain poorly understood. In our studies, we utilized transgenic methods, pain behavioral assessments, and single-cell RNA sequencing (scRNAseq) in a clinically relevant high-fat diet (HFD) mouse model of PDN and skin biopsy samples from PDN patients, to investigate the role of epidermal Langerhans cells (LCs) in this condition. We observed an increased density of LCs in the skin of PDN male mice coinciding with the onset of mechanical allodynia. Furthermore, we found that LCs density correlated with small fiber degeneration in HFD mice and skin biopsies taken from well-characterized PDN patients compared to healthy controls. Importantly, the selective ablation of LCs using a diphtheria-toxin strategy was found to prevent nociceptive behavior in male mice. This indicates that LCs are both necessary and sufficient for the development of mechanical allodynia and spontaneous pain in HFD male mice. Interestingly, when LCs were ablated in HFD female mice, it did not prevent but rather promoted pain behavior, suggesting the existence of sex-specific mechanisms mediated by LCs. scRNAseq transcriptomic analysis of the paw epidermis from HFD mice, which included both males and females, revealed significant sex-mediated differences in the expression of specific target genes within this PDN model. In male mice, scRNAseq identified differentially expressed genes associated with axonal guidance and immune responses in LCs. Additionally, by integrating single-cell RNA data from the epidermis and dorsal root ganglia (DRG) of male mice, we uncovered altered communication between LCs and cutaneous afferents through Semaphorin-Plexin signaling pathways in PDN. These findings highlight LCs as key contributors to the development of PDN and suggest their potential as therapeutic targets for innovative treatments, particularly topical therapies aimed at modulating immune cell activity and neuroimmune communication in the skin. Our investigations of male and female mice indicate that LCs may play different roles in mechanical sensation under both normal and pathological conditions, such as PDN. This underscores the importance of considering sex differences when developing more effective treatments. BRIEF SUMMARYThe interaction between non-neuronal cells and nerve endings in the epidermis plays a significant role in the development of Painful Diabetic Neuropathy (PDN), a challenging complication of diabetes. Using transgenic methods, pain behavioral assessments, and single-cell transcriptomics in a clinically relevant high-fat diet (HFD) mouse model of PDN, and in skin tissues from PDN patients, we identified epidermal Langerhans cells (LCs) as key contributors to the development of PDN. In these studies, we revealed, for the first time, a dimorphic role for LCs in the development of mechanical allodynia and spontaneous pain in a HFD model of PDN. Single-cell RNA sequencing (scRNAseq) of the paw epidermis from both male and female HFD mice identified sex-mediated differences in the expression of specific target genes within this PDN model. In male mice, scRNAseq of LCs revealed differentially expressed genes related to immune responses and axonal guidance, including Semaphorin-Plexin signaling pathways.

neuroscience↗

bioRxiv2024

Transcriptomic analysis and high throughput functional characterization of human induced pluripotent stem cell derived sensory neurons

Peripheral sensory neurons are a primary effector in pain neurotransmission, and have become a useful cellular model for the study of pain. While rodent tissue has historically served as a source of these neurons, it has become increasingly clear that pain mechanisms in rodents and humans are substantially divergent. Sensory neurons harvested from cadaveric human tissue serve as a superior translational model for studying pain mechanisms, however their relative paucity limits their widespread utility. Theoretically, sensory neurons manufactured from human pluripotent stem cells (hPSCs) could help bridge this translational gap given their relative abundance and potential similarity to primary human tissue. However, hPSC-derived sensory neurons manufactured with the most common methodologies correlate poorly to human tissue both transcriptionally and functionally. In the present work, we compare a novel population of hPSC-derived sensory neurons to previously published datasets and find this novel population to more closely resemble human primary dorsal root ganglia transcriptionally. Furthermore, we evaluate the heterogeneity of this novel population via single nucleus RNA sequencing and find it resembles specific nociceptor and mechanoreceptor subsets found in vivo. Finally, we assay the functionality of this population with high throughput automated patch clamp electrophysiology with respect to voltage-gated sodium (Nav) and potassium channels (Kv), and ligand-gated ionotropic GABA and P2X receptors. Overall, we find this population of hPSC-derived sensory neurons to be of relatively high fidelity, and suitable for interrogating numerous potential pain targets on a fully humanized platform.

cell biology↗

bioRxiv2023

NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

ObjectiveThe safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. ApproachA literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main ResultsWe compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. SignificanceThis novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

neuroscience↗

bioRxiv2022

Cfdp1 is Essential for Cardiac Development and Function

Cardiovascular diseases (CVDs) are the prevalent cause of mortality worldwide and account for the most common noncommunicable disease. CVDs describe a wide spectrum of disorders affecting the proper function, physiology and morphogenesis of the heart and blood vessels. The risk of developing cardiovascular diseases is modulated by a combination of environmental and genetic effectors. Thus, its highly important to identify candidate genes and elucidate their role in the manifestation of the disease. Large-scale human studies have revealed the implication of Craniofacial Development Protein 1 (CFDP1) in coronary artery disease (CAD). CFDP1 belongs to the evolutionary conserved Bucentaur (BCNT) family and up to date, its function and mechanism of action in Cardiovascular Development is still unclear. In this study, we utilize zebrafish to investigate the role of cfdp1 in the developing heart due to the high genomic homology, similarity in heart physiology and the ease of experimentally manipulation. We showed that cfdp1 is expressed during development and at 120 hours post fertilization its expression is restricted to the region of the heart and the head. We then generated a cfdp1-null zebrafish line using CRISPR-Cas9 system which led to a lethal phenotype since knockout embryos do not reach adulthood. cfdp1-/- embryos develop arrhythmic hearts and defective cardiac performance exhibiting statistically significant differences in heart features including End Diastolic Volume, Cardiac Output, Ejection Fraction and Stroke Volume. Myocardial trabeculation is also impaired in cfdp1-/- embryonic hearts, implying its regulatory role also in this developmental process. Findings from both knockdown and knockout experiments showed that abrogation of cfdp1 leads to downregulation of Wnt signaling in embryonic hearts during valve development but without affecting Notch activation in this process.

developmental biology↗

bioRxiv2021

Learning attention-controllable border-ownership for objectness inference and binding

Human visual systems can parse a scene composed of novel objects and infer their surfaces and occlusion relationships without relying on object-specific shapes or textures. Perceptual grouping can bind together spatially disjoint entities to unite them as one object even when the object is entirely novel, and bind other perceptual properties like color and texture to that object using object-based attention. Border-ownership assignment, the assignment of perceived occlusion boundaries to specific perceived surfaces, is an intermediate representation in the mammalian visual system that facilitates this perceptual grouping. Since objects in a scene can be entirely novel, inferring border ownership requires integrating global figural information, while dynamically postulating what the figure is, a chicken-and egg process that is complicated further by missing or conflicting local evidence regarding the presence of boundaries. Based on neuroscience observations, we introduce a model - the cloned Markov random field (CMRF)- that can learn attention-controllable representations for border-ownership. Higher-order contour representations that distinguish border-ownerships emerge as part of learning in this model. When tested with a cluttered scene of novel 2D objects with noisy contour-only evidence, the CMRF model is able to perceptually group them, despite clutter and missing edges. Moreover, the CMRF is able to use occlusion cues to bind disconnected surface elements of novel objects into coherent objects, and able to use top-down attention to assign border ownership to overlapping objects. Our work is a step towards dynamic binding of surface elements into objects, a capability that is crucial for intelligent agents to interact with the world and to form entity-based abstractions.

neuroscience↗

bioRxiv2020

A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. Theory-driven efforts will be required to tease apart the functional logic of cortical circuits from the vast amounts of experimental data on cortical connectivity and physiology. Although the theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation, making precise and falsifiable biological mappings need models that tackle the challenge of real world tasks. Based on a recent generative model, Recursive Cortical Networks, that demonstrated excellent performance on visual task benchmarks, we derive a family of anatomically instantiated and functional cortical circuit models. Efficient inference and generalization guided the representational choices in the original computational model. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feed-forward, feedback, and lateral connections observed in different laminae and columns, assigns a computational role for the path through the thalamus, predicts the interactions between blobs and inter-blobs, and offers an algorithmic explanation for the innate inter-laminar connectivity between clonal neurons within a cortical column. The model also explains several visual phenomena, including the subjective contour effect, and neon-color spreading effect, with circuit-level precision. Our work paves a new path forward in understanding the logic of cortical and thalamic circuits.

neuroscience↗

bioRxiv2019

Learning cognitive maps for vicarious evaluation

Cognitive maps are mental representations of spatial and conceptual relationships in an environment. These maps are critical for flexible behavior as they permit us to navigate vicariously, but their underlying representation learning mechanisms are still unknown. To form these abstract maps, hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization, efficient planning, and handling of uncertainty. Here we introduce a specific higher-order graph structure - clone-structured cognitive graph (CSCG) - which forms different clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a novel probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety cognitive map phenomena such as discovering spatial relations from an aliased sensory stream, transitive inference between disjoint episodes of experiences, formation of transferable structural knowledge, and shortcut-finding in novel environments. By learning different clones for different contexts, CSCGs explain the emergence of splitter cells and route-specific encoding of place cells observed in maze navigation, and event-specific graded representations observed in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for a variety of place cell remapping phenomena. By lifting the aliased observations into a hidden space, CSCGs reveal latent modularity that is then used for hierarchical abstraction and planning. Altogether, learning and inference using a CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.

neuroscience↗

bioRxiv2019

Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

Hippocampus encodes cognitive maps that support episodic memories, navigation, and planning. Under-standing the commonality among those maps as well as how those maps are structured, learned from experience, and used for inference and planning is an interesting but unsolved problem. We propose higher-order graphs as the general principle and present, as a plausible model, a cloned hidden Markov model (HMM) that can learn these graphs efficiently from experienced sequences. In our experiments, we use the cloned HMM for learning spatial and abstract representations. We show that inference and planning in the learned CHMM encapsulates many of the key properties of hippocampal cells observed in rodents and humans. Cloned HMM thus provides a new frame-work for understanding hippocampal function.

neuroscience↗

bioRxiv2018

Cortical Microcircuits from a Generative Vision Model

Understanding the information processing roles of cortical circuits is an outstanding problem in neuroscience and artificial intelligence. The theoretical setting of Bayesian inference has been suggested as a framework for understanding cortical computation. Based on a recently published generative model for visual inference (George et al., 2017), we derive a family of anatomically instantiated and functional cortical circuit models. In contrast to simplistic models of Bayesian inference, the underlying generative models representational choices are validated with real-world tasks that required efficient inference and strong generalization. The cortical circuit model is derived by systematically comparing the computational requirements of this model with known anatomical constraints. The derived model suggests precise functional roles for the feedforward, feedback and lateral connections observed in different laminae and columns, and assigns a computational role for the path through the thalamus.

neuroscience↗

bioRxiv2018

Explaining Visual Cortex Phenomena using Recursive Cortical Network

The connectivity and information pathways of visual cortex are well studied, as are observed physiological phenomena, yet a cohesive model for explaining visual cortex processes remains an open problem. For a comprehensive understanding, we need to build models of the visual cortex that are capable of robust real-world performance, while also being able to explain psychophysical and physiological observations. To this end, we demonstrate how the Recursive Cortical Network (George et al., 2017) can be used as a computational model to reproduce and explain subjective contours, neon color spreading, occlusion vs. deletion, and the border-ownership competition phenomena observed in the visual cortex.

neuroscience↗